REST: a reliable estimation of stopping time algorithm for social game experiments

Abstract

Through a social game, we integrate building occupants into the control and management of an office building that is instrumented with networked embedded systems for sensing and actuation. The goal of the social game is to both incentivize building occupants to be more energy efficient and learn behavioral models for occupants so that the building can be made sustainable through automation. Given a generative model for the occupants behavior in the competitive environment created by the social game, we develop a method for learning the parameters of the behavioral model as we conduct the experiment by adopting a learning to learn framework. Using tools from statistical learning, we provide bounds on the parameter inference error. In addition, we provide an algorithm for computing the stopping time required for a specified level of confidence in estimation. We show the performance of our algorithm in several examples.